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The sharp bits 🔪

This page highlight common pitfalls that users may encounter when learning to use dynamiqs.

import dynamiqs as dq

Main differences with QuTiP

The syntax in dynamiqs is similar to QuTiP, a popular Python library for quantum simulation. However, there are some important differences that you should be aware of.

Floating-point precision

In dynamiqs, all arrays are represented by default with single-precision floating-point numbers (float32 or complex64), whereas the default in QuTiP or NumPy is double-precision (float64 or complex128). We made this choice to match JAX's default, and for performance reasons, as many problems do not require double-precision. If needed, it is possible to switch to double-precision using dq.set_precision():

dq.set_precision('double')  # 'simple' by default

When using single-precision, there are certain limitations to be aware of:

  • Large numbers: Numerical errors in floating-point arithmetic become more significant when using large numbers. Therefore, you should try to choose units for your simulation such that all quantities involved are not too large.
  • Tolerances: If you require very precise simulation results (e.g. if you set lower rtol and atol than the default values), the simulation time may increase significantly, and simulations may even get stuck. In such cases, it is recommended to switch to double-precision.


Most GPUs do not have native support for double-precision, and only perform well in single-precision. However, note that some recent NVIDIA GPUs (e.g. V100, A100, H100) do provide efficient support for double-precision.

Adding a scalar to an operator

In QuTiP, adding a scalar to a Qobj performs an implicit multiplication of the scalar with the identity matrix. This convention differs from the one adopted by common scientific libraries such as NumPy, PyTorch or JAX. In dynamiqs, adding a scalar to an array performs an element-wise addition. To achieve the same result as in QuTiP, you must explicitly multiply the scalar with the identity matrix:

>>> sz = dq.sigmaz()
>>> sz - 2 * dq.eye(2)
Array([[-1.+0.j,  0.+0.j],
       [ 0.+0.j, -3.+0.j]], dtype=complex64)
>>> sz = dq.sigmaz()
>>> sz - 2
Array([[-1.+0.j, -2.+0.j],
       [-2.+0.j, -3.+0.j]], dtype=complex64)

Multiplying two operators

In QuTiP, the * symbol is used to multiply two operators. This convention also differs from common scientific libraries. In dynamiqs, the @ symbol is used for matrix multiplication, and the * symbol is reserved for element-wise multiplication:

>>> sx = dq.sigmax()
>>> sx @ sx
Array([[1.+0.j, 0.+0.j],
       [0.+0.j, 1.+0.j]], dtype=complex64)
>>> sx = dq.sigmax()
>>> sx * sx
Array([[0.+0.j, 1.+0.j],
       [1.+0.j, 0.+0.j]], dtype=complex64)

Likewise, you should use dq.powm() instead of ** (element-wise power) to compute the power of a matrix:

>>> dq.powm(sx, 2)
Array([[1.+0.j, 0.+0.j],
       [0.+0.j, 1.+0.j]], dtype=complex64)
>>> sx**2
Array([[0.+0.j, 1.+0.j],
       [1.+0.j, 0.+0.j]], dtype=complex64)

Computing matrix adjoint

Use dq.dag(x) or x.mT.conj() instead of x.dag() to get the hermitian conjugate of x.

Why is there no .dag() method in dynamiqs?

To guarantee optimum performances and straightforward compatibility with the JAX ecosystem, dynamiqs does not subclass JAX arrays. As a consequence, we can't define a custom .dag() method on arrays. Note that this will possibly change in the future, as we are working on an extension that will allow defining custom methods on arrays.

Using a for loop

If you want to simulate multiple Hamiltonians or initial states, you should use batching instead of a for loop. We explain in detail how it works in the Batching simulations tutorial, and the associated gain in performance.